The world is shifting from cloud-based AI servers to AI-ready computers. The aim is to make AI systems faster, more energy-efficient, and more secure—especially when it comes to sending sensitive information to cloud servers. 

In our new article series, we will focus on the functionalities of Neural Processing Units (NPUs) and Central Processing Units (CPUs) and explore their role in the future of AI processing. So, which one outshines the other? Let’s explore their key differences.

The distinction between Central Processing Units (CPUs) and Neural Processing Units (NPUs) extends beyond their primary functions, touching upon aspects such as purpose, versatility, performance, efficiency, and cost. Understanding these differences is crucial for appreciating the unique contributions of each processor type to the technological ecosystem.

 

Purpose

The broad application spectrum of CPUs contrasts sharply with the dedicated AI focus of NPUs. CPUs are designed to be highly versatile and capable of performing a wide range of computing tasks, from running operating systems to executing user applications. This general-purpose design ensures that CPUs can manage the diverse requirements of computing devices.

In contrast, NPUs are purpose-built to accelerate artificial intelligence tasks, particularly those involving neural networks. Their architecture is optimised for the parallel processing demands of AI algorithms, making them highly specialised tools in the AI domain.

 

Versatility

This specialisation impacts the versatility of the two processor types. CPUs are like Swiss Army knives, equipped to handle many tasks reasonably efficiently. This versatility makes them indispensable in computing, serving as the foundational processing unit in most electronic devices. NPUs, however, are more like scalpel blades, designed with a singular focus on AI tasks. Their specialised nature means they excel in AI functions but are unsuitable for general computing tasks.

 

Performance

When it comes to AI performance, the limitations of CPUs become apparent. While capable of performing AI-related computations, CPUs cannot match the optimised efficiency of NPUs. The specialised architecture of NPUs allows for faster processing of AI algorithms, significantly reducing the time required for tasks such as training deep learning models. This optimised performance is a key advantage of NPUs, enabling more complex and capable AI systems.

 

Efficiency

In terms of power consumption, efficiency is another area where NPUs have an edge. The specialised design of NPUs allows them to perform AI tasks with greater energy efficiency than CPUs. This is particularly important in large-scale AI applications, where the energy costs of training and running AI models can be substantial. NPUs can help mitigate these costs, offering a more sustainable solution for AI processing.

 

Cost

Cost considerations play a significant role in choosing between CPUs and NPUs. The general-purpose nature of CPUs means they benefit from economies of scale, making them relatively affordable for a wide range of applications. NPUs, being specialised processors, may come with a higher initial cost due to their specialised design and manufacturing process. However, the cost must be weighed against AI application performance and efficiency gains. For organisations heavily invested in AI, the long-term benefits of NPUs, such as reduced operational costs and enhanced capabilities, may justify the investment.

In conclusion, while CPUs and NPUs serve distinct roles within the computing landscape, both are integral to the advancement of technology. The choice between them depends on the application's specific needs, with CPUs offering broad versatility and NPUs providing optimised performance for AI tasks. As AI continues to evolve, the interplay between these processors will be crucial in shaping the future of computing.

 

When to choose which: Real-world use cases

The decision between deploying Central Processing Units (CPUs) and Neural Processing Units (NPUs) hinges on the specific requirements of the task at hand. Understanding the strengths and limitations of each processor type is crucial for optimising performance, efficiency, and cost-effectiveness. Here, we explore real-world scenarios that illustrate when to choose one over the other.

 

Scenarios where CPUs are suitable

General computing: CPUs are the go-to choice for everyday computing tasks such as word processing, web browsing, and basic data analysis. Their versatility and efficiency in handling various applications make them ideal for standard computing needs.

Simple AI tasks: CPUs can also manage more straightforward AI tasks, such as basic machine learning models for predictive text or simple data predictions. In scenarios where the AI demand is not intensive, CPUs can perform adequately without needing specialised hardware.

 

Ideal use cases for NPUs

Image recognition: NPUs excel in applications requiring rapid and efficient visual data processing. Image recognition systems, such as those used in security cameras for facial recognition or in social media for photo tagging, benefit immensely from the parallel processing capabilities of NPUs.

Self-driving cars: Autonomous vehicles rely on real-time data processing to make split-second decisions. NPUs are critical in processing vast amounts of sensory data and executing complex algorithms required for safe and efficient operation.

Medical diagnosis: In the medical field, NPUs are revolutionising diagnostic processes. They are used in analysing medical images, such as X-rays and MRIs, with deep learning models to identify patterns that may indicate diseases. The speed and accuracy of NPUs in processing these large datasets can significantly aid in early diagnosis and treatment planning.

 

Emphasising the importance of understanding specific needs

A thorough understanding of the application's specific needs should guide the choice between CPUs and NPUs. Factors such as the complexity of the task, the volume of data to be processed, the required speed of processing, and energy consumption considerations all play a role in determining the most suitable processor type.

CPUs offer the necessary versatility and efficiency for tasks requiring general computing power and flexibility. However, for applications that demand high-speed processing of AI algorithms and large datasets, NPUs provide a specialised solution that significantly enhances performance and efficiency.

In conclusion, using CPUs or NPUs is not a one-size-fits-all scenario. It requires a nuanced understanding of the application's demands and a strategic approach to leveraging the strengths of each processor type. By carefully considering the specific requirements of their use cases, developers and organisations can make informed choices that optimise their technological capabilities and drive innovation forward.

Next week’s blog will examine the future of AI processing. Meanwhile, contact us for further information on our AI-ready computer offerings.

Have you read the previous article of our series? Click here to read it.